GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images

Hemalatha Gunasekaran; Krishnamoorthi Ramalakshmi; Deepa Kanmani Swaminathan; Andrew J; Manuel Mazzara

doi:10.3390/bioengineering10070809

GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images

Bioengineering (Basel). 2023 Jul 5;10(7):809. doi: 10.3390/bioengineering10070809.

Authors

Hemalatha Gunasekaran¹, Krishnamoorthi Ramalakshmi², Deepa Kanmani Swaminathan³, Andrew J⁴, Manuel Mazzara⁵

Affiliations

¹ Information Technology, University of Technology and Applied Sciences, Ibri 516, Oman.
² Information Technology, Alliance College of Engineering and Design, Alliance University, Bengaluru 562106, India.
³ Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore 641008, India.
⁴ Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
⁵ Institute of Software Development and Engineering, Innopolis University, 420500 Innopolis, Russia.

Abstract

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes. When individual models are used for classification, they are often prone to misclassification since they may not be able to learn the characteristics of all the classes adequately. This is due to the fact that each model may learn the characteristics of specific classes more efficiently than the other classes. We propose an ensemble model that leverages the predictions of three pre-trained models, DenseNet201, InceptionV3, and ResNet50 with accuracies of 94.54%, 88.38%, and 90.58%, respectively. The predictions of the base learners are combined using two methods: model averaging and weighted averaging. The performances of the models are evaluated, and the model averaging ensemble has an accuracy of 92.96% whereas the weighted average ensemble has an accuracy of 95.00%. The weighted average ensemble outperforms the model average ensemble and all individual models. The results from the evaluation demonstrate that utilizing an ensemble of base learners can successfully classify features that were incorrectly learned by individual base learners.

Keywords: base learners; deep learning; ensemble learning; gastrointestinal tract; transfer learning.

Grants and funding

This research received no external funding.